The research work “Weed Object Detection” aims to create an automated system that leverages machine learning algorithms and advanced computer vision techniques to accurately identify and separate weeds from crops in agricultural fields. The system employs various image processing methods, such as contour detection, bounding box extraction, and Histogram of Oriented Gradients (HOG) feature extraction, to precisely detect and isolate weed patches in images captured from crop fields. This approach enhances the accuracy of weed detection by focusing on specific visual features of weeds. The improvement in precision, there is an incorporation of several machine learning (supervised learning algorithms) K-Nearest Neighbors (KNN), Random Forest (RF), Support Vector Machine (SVM), and Decision Tree (DT) classifiers. These algorithms are trained on labeled datasets to effectively distinguish weeds from crops, enabling the system to achieve high accuracy in identifying and managing weed growth in diverse agricultural environments. The proposed model offers substantial benefits to farmers and agricultural professionals by providing them with detailed insights into weed management. It is designed to be both flexible and scalable, accommodating varying weed densities, and coverage across different types of fields. Automating the weed detection process, the system reduces the manual effort required for weed control, potentially leading to increased crop yields and more efficient field management. This innovation is a step towards enhancing sustainable farming practices through the integration of technology.